IEEE Project Abstract

Internet-of-Things (IoT) has connected billions of devices to the Internet. These devices are already collecting zettabytes (1021) of data. However, the current IoT framework suffers from limited sensor energy, communication bandwidth, and server storage. These limitations impede the ability to send all the sensor data to the server all the time. Compact smart sensors provide a way to address this challenge. As opposed to the conventional sense-and-transmit sensors, emerging smart sensors can collect data, extract features, derive local inferences, and transmit only inference outcomes and possibly some raw data associated with rare events instead of all the raw data. This can dramatically cut down on the amount of sensor data transmitted, and hence its communication energy and network traffic. However, edge or server inference models trained with conventional machine learning approaches do not account for the fact that the smart sensors in the system have already performed a local inference. These approaches need all the sensor data and hence only cater to the traditional sense-and-transmit paradigm. This undoes the energy benefits brought about by smart sensors. In this paper, we propose a hierarchical inference model for IoT applications based on hierarchical learning and local inferences. Our model is able to take advantage of inference already performed on smart sensors, while at the same time accommodating conventional sense-and-transmit sensors in the IoT system. It also generalizes sensor-level inference to inference at other edge nodes by exploiting the intrinsically sensor/edge-grouped IoT data structure. We train classifiers hierarchically, aligned with the sensor-edge-server IoT paradigm. We verify our approach with seven IoT applications, demonstrating that the model is accurate, efficient, and generally applicable. We derive four edge-level inference models and four server-level inference models for these applications. For the four edge-level inference models, we reduce the number of bits transmitted from the sensor by 3.2×-42.7× while at the same time also improving the classification accuracy by 0.3-6.7 percent. For the four server-level inference models, we reduce the number of bits transmitted from the sensor by 3.2×-42.7× while at the same time also improving the classification accuracy by 0.3%-6.7%. For the four server-level inference models, we reduce the number of edge-to-server bits transmitted by 17×-60×, with classification accuracy change in the −0.4%-+0.1% range.